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109 lines
5.4 KiB
109 lines
5.4 KiB
## Neural networks in openpilot
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To view the architecture of the ONNX networks, you can use [netron](https://netron.app/)
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## Supercombo
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### Supercombo input format (Full size: 393738 x float32)
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* **image stream**
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* Two consecutive images (256 * 512 * 3 in RGB) recorded at 20 Hz : 393216 = 2 * 6 * 128 * 256
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* Each 256 * 512 image is represented in YUV420 with 6 channels : 6 * 128 * 256
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* Channels 0,1,2,3 represent the full-res Y channel and are represented in numpy as Y[::2, ::2], Y[::2, 1::2], Y[1::2, ::2], and Y[1::2, 1::2]
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* Channel 4 represents the half-res U channel
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* Channel 5 represents the half-res V channel
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* **desire**
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* one-hot encoded vector to command model to execute certain actions, bit only needs to be sent for 1 frame : 8
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* **traffic convention**
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* one-hot encoded vector to tell model whether traffic is right-hand or left-hand traffic : 2
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* **recurrent state**
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* The recurrent state vector that is fed back into the GRU for temporal context : 512
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### Supercombo output format (Full size: 6472 x float32)
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* **plan**
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* 5 potential desired plan predictions : 4955 = 5 * 991
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* predicted mean and standard deviation of the following values at 33 timesteps : 990 = 2 * 33 * 15
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* x,y,z position in current frame (meters)
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* x,y,z velocity in local frame (meters/s)
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* x,y,z acceleration local frame (meters/(s*s))
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* roll, pitch , yaw in current frame (radians)
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* roll, pitch , yaw rates in local frame (radians/s)
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* probability[^1] of this plan hypothesis being the most likely: 1
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* **lanelines**
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* 4 lanelines (outer left, left, right, and outer right): 528 = 4 * 132
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* predicted mean and standard deviation for the following values at 33 x positions : 132 = 2 * 33 * 2
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* y position in current frame (meters)
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* z position in current frame (meters)
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* **laneline probabilties**
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* 2 probabilities[^1] that each of the 4 lanelines exists : 8 = 4 * 2
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* deprecated probability
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* used probability
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* **road-edges**
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* 2 road-edges (left and right): 264 = 2 * 132
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* predicted mean and standard deviation for the following values at 33 x positions : 132 = 2 * 33 * 2
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* y position in current frame (meters)
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* z position in current frame (meters)
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* **leads**
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* 2 hypotheses for potential lead cars : 102 = 2 * 51
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* predicted mean and stadard deviation for the following values at 0,2,4,6,8,10s : 48 = 2 * 6 * 4
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* x position of lead in current frame (meters)
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* y position of lead in current frame (meters)
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* speed of lead (meters/s)
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* acceleration of lead(meters/(s*s))
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* probabilities[^1] this hypothesis is the most likely hypothesis at 0s, 2s or 4s from now : 3
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* **lead probabilities**
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* probability[^1] that there is a lead car at 0s, 2s, 4s from now : 3 = 1 * 3
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* **desire state**
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* probability[^1] that the model thinks it is executing each of the 8 potential desire actions : 8
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* **meta** [^2]
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* Various metadata about the scene : 80 = 1 + 35 + 12 + 32
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* Probability[^1] that openpilot is engaged : 1
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* Probabilities[^1] of various things happening between now and 2,4,6,8,10s : 35 = 5 * 7
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* Disengage of openpilot with gas pedal
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* Disengage of openpilot with brake pedal
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* Override of openpilot steering
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* 3m/(s*s) of deceleration
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* 4m/(s*s) of deceleration
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* 5m/(s*s) of deceleration
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* Probabilities[^1] of left or right blinker being active at 0,2,4,6,8,10s : 12 = 6 * 2
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* Probabilities[^1] that each of the 8 desires is being executed at 0,2,4,6s : 32 = 4 * 8
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* **pose** [^2]
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* predicted mean and standard deviation of current translation and rotation rates : 12 = 2 * 6
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* x,y,z velocity in current frame (meters/s)
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* roll, pitch , yaw rates in current frame (radians/s)
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* **recurrent state**
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* The recurrent state vector that is fed back into the GRU for temporal context : 512
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[^1]: All probabilities are in logits, so you need to apply sigmoid or softmax functions to get actual probabilities
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[^2]: These outputs come directly from the vision blocks, they do not have access to temporal state or the desire input
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## Driver Monitoring Model
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* .onnx model can be run with onnx runtimes
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* .dlc file is a pre-quantized model and only runs on qualcomm DSPs
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### input format
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* single image (640 * 320 * 3 in RGB):
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* full input size is 6 * 640/2 * 320/2 = 307200
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* represented in YUV420 with 6 channels:
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* Channels 0,1,2,3 represent the full-res Y channel and are represented in numpy as Y[::2, ::2], Y[::2, 1::2], Y[1::2, ::2], and Y[1::2, 1::2]
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* Channel 4 represents the half-res U channel
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* Channel 5 represents the half-res V channel
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* normalized, ranging from -1.0 to 1.0
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### output format
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* 39 x float32 outputs ([parsing example](https://github.com/commaai/openpilot/blob/master/selfdrive/modeld/models/dmonitoring.cc#L165))
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* face pose: 12 = 6 + 6
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* face orientation [pitch, yaw, roll] in camera frame: 3
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* face position [dx, dy] relative to image center: 2
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* normalized face size: 1
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* standard deviations for above outputs: 6
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* face visible probability: 1
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* eyes: 20 = (8 + 1) + (8 + 1) + 1 + 1
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* eye position and size, and their standard deviations: 8
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* eye visible probability: 1
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* eye closed probability: 1
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* wearing sunglasses probability: 1
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* poor camera vision probability: 1
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* face partially out-of-frame probability: 1
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* (deprecated) distracted probabilities: 2
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* face covered probability: 1
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